dc.contributor.author | Chugh, T | |
dc.contributor.author | Ymeraj, E | |
dc.date.accessioned | 2022-04-06T14:18:25Z | |
dc.date.issued | 2022-07-19 | |
dc.date.updated | 2022-04-06T13:59:39Z | |
dc.description.abstract | Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using multi-objective Bayesian optimisation (BO). However, the traditional BO cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of multi-objective BO to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based multi-objective BO. | en_GB |
dc.description.sponsorship | University of Exeter | en_GB |
dc.identifier.citation | GECCO 2022: Genetic and Evolutionary Computation Conference, 9 - 13 July 2022, Boston, US, pp. 695 - 698 | en_GB |
dc.identifier.doi | https://doi.org/10.1145/3520304.3528951 | |
dc.identifier.uri | http://hdl.handle.net/10871/129288 | |
dc.identifier | ORCID: 0000-0001-5123-8148 (Chugh, Tinkle) | |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery (ACM) | en_GB |
dc.rights | © 2022 Copyright held by the owner/author(s). | |
dc.subject | Surrogate modelling | en_GB |
dc.subject | Gaussian process | en_GB |
dc.subject | Renewable Energy | en_GB |
dc.subject | Uncertainty quantification | en_GB |
dc.subject | Gaussian Process Over sets | en_GB |
dc.subject | Pareto optimality | en_GB |
dc.title | Wind farm layout optimisation using set based multi-objective Bayesian optimisation | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2022-04-06T14:18:25Z | |
exeter.location | Boston, MA, USA | |
dc.description | This is the author accepted manuscript. The final version is available from ACM via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2022-03-24 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2022-03-24 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2022-04-06T13:59:41Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-07-29T09:04:33Z | |
refterms.panel | B | en_GB |